Genome-wide association and linkage studies involving hundreds or thousands of Single Nucleotide[unreadable] Polymorphisms (SNPs) are becoming increasingly common due to the rapid development of[unreadable] biotechnologies. Among many statistical challenges arising from these studies, the typical limited sample[unreadable] size is of particular concern because of high genotyping cost putting pressure to limit the number of[unreadable] individuals genotyped; increased statistical significant level for fear of too many false positives due to[unreadable] multiple comparisons; and moderate risk from each disease-associated variant allele in complex diseases.[unreadable] This application considers two strategies to address this issue: (1) to increase sample size by pooling data[unreadable] obtained from several sources; (2) to devise better statistical and computational tools for more efficient[unreadable] usage of the data. Correspondly, the first aim is to develop estimation and inference procedures for genetic[unreadable] association using data obtained from both population-based case-control and family-based studies,[unreadable] accommodating diverse ascertainment schemes of cases and controls, whereas the second aim is to[unreadable] develop analysis and regularization methods that enhance the possibility that the disease-associated[unreadable] variants and their interactions can actually be identified. The second aim is also concerned with the[unreadable] construction of risk predictive models from these SNPs.[unreadable] The highly dense SNP markers also pose problems to a more traditional model-based linkage analysis for[unreadable] gene discovery, because the methods for this analysis were developed assuming markers in linkage[unreadable] equilibrium, an assumption that is likely violated with the density of the SNPs. The third aim is to develop[unreadable] and evaluate estimating procedures for multipoint linkage analysis in the presence of linkage disequilibrium[unreadable] among SNP markers.